





'''simple docstring'''






import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


lowerCAmelCase									=   ["""bart.large""", """bart.large.mnli""", """bart.large.cnn""", """bart_xsum/model.pt"""]
lowerCAmelCase									=   {"""bart.large""": BartModel, """bart.large.mnli""": BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse("""0.9.0"""):
					raise Exception("""requires fairseq >= 0.9.0""")


logging.set_verbosity_info()
lowerCAmelCase									=   logging.get_logger(__name__)

lowerCAmelCase									=   """ Hello world! cécé herlolip"""

lowerCAmelCase									=   [
    ("""model.classification_heads.mnli.dense.weight""", """classification_head.dense.weight"""),
    ("""model.classification_heads.mnli.dense.bias""", """classification_head.dense.bias"""),
    ("""model.classification_heads.mnli.out_proj.weight""", """classification_head.out_proj.weight"""),
    ("""model.classification_heads.mnli.out_proj.bias""", """classification_head.out_proj.bias"""),
]






def   __A    (				a_    :	Optional[Any]						):
		lowerCAmelCase  :     Dict       						= [
		    "encoder.version",
		    "decoder.version",
		    "model.encoder.version",
		    "model.decoder.version",
		    "_float_tensor",
		]
		for k in ignore_keys:
				state_dict.pop(a_   ,a_						)






def   __A    (				a_    :	Tuple   ,a_    :	Tuple   ,a_    :	Union[str, Any]						):
		lowerCAmelCase  :     Tuple       						= dct.pop(a_						)
		lowerCAmelCase  :     List[Any]       						= val






def   __A    (				a_    :	List[Any]						):
		lowerCAmelCase  :     Optional[int]       						= torch.load(a_   ,map_location="cpu"						)
		lowerCAmelCase  :     Any       						= torch.hub.load("pytorch/fairseq"   ,"bart.large.cnn"						).eval()
		hub_interface.model.load_state_dict(sd["model"]						)
		return hub_interface






def   __A    (				a_    :	Union[str, Any]						):
		lowerCAmelCase						,		lowerCAmelCase  :     Union[str, Any]       						= emb.weight.shape
		lowerCAmelCase  :     int       						= nn.Linear(a_   ,a_   ,bias=a_						)
		lowerCAmelCase  :     Dict       						= emb.weight.data
		return lin_layer






@torch.no_grad()
def   __A    (				a_    :	Tuple   ,a_    :	Tuple   ,a_    :	Union[str, Any]=None						):
		if not os.path.exists(a_						):
				lowerCAmelCase  :     List[Any]       						= torch.hub.load("pytorch/fairseq"   ,a_						).eval()
		else:
				lowerCAmelCase  :     Tuple       						= load_xsum_checkpoint(a_						)

		bart.model.upgrade_state_dict(bart.model.state_dict()						)
		if hf_checkpoint_name is None:
				lowerCAmelCase  :     List[str]       						= checkpoint_path.replace("."   ,"-"						)
		lowerCAmelCase  :     Optional[int]       						= BartConfig.from_pretrained(a_						)
		lowerCAmelCase  :     Union[str, Any]       						= bart.encode(a_						).unsqueeze(0						)
		lowerCAmelCase  :     str       						= BartTokenizer.from_pretrained(a_						).encode(a_   ,return_tensors="pt"						).unsqueeze(0						)
		if not torch.eq(a_   ,a_						).all():
				raise ValueError(
				    f'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}'''						)

		if checkpoint_path == "bart.large.mnli":
				lowerCAmelCase  :     List[Any]       						= bart.state_dict()
				remove_ignore_keys_(a_						)
				lowerCAmelCase  :     List[Any]       						= state_dict["model.decoder.embed_tokens.weight"]
				for src, dest in mnli_rename_keys:
						rename_key(a_   ,a_   ,a_						)
				lowerCAmelCase  :     str       						= BartForSequenceClassification(a_						).eval()
				model.load_state_dict(a_						)
				lowerCAmelCase  :     Optional[Any]       						= bart.predict("mnli"   ,a_   ,return_logits=a_						)
				lowerCAmelCase  :     Optional[int]       						= model(a_						)[0]  # logits
		else:  # no classification heads to worry about
				lowerCAmelCase  :     List[str]       						= bart.model.state_dict()
				remove_ignore_keys_(a_						)
				lowerCAmelCase  :     List[Any]       						= state_dict["decoder.embed_tokens.weight"]
				lowerCAmelCase  :     Any       						= bart.extract_features(a_						)
				if hf_checkpoint_name == "facebook/bart-large":
						lowerCAmelCase  :     Optional[Any]       						= BartModel(a_						).eval()
						model.load_state_dict(a_						)
						lowerCAmelCase  :     Optional[Any]       						= model(a_						).model[0]
				else:
						lowerCAmelCase  :     Dict       						= BartForConditionalGeneration(a_						).eval()  # an existing summarization ckpt
						model.model.load_state_dict(a_						)
						if hasattr(a_   ,"lm_head"						):
								lowerCAmelCase  :     int       						= make_linear_from_emb(model.model.shared						)
						lowerCAmelCase  :     Dict       						= model.model(a_						)[0]

    # Check results
		if fairseq_output.shape != new_model_outputs.shape:
				raise ValueError(
				    f'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}'''						)
		if (fairseq_output != new_model_outputs).any().item():
				raise ValueError("Some values in `fairseq_output` are different from `new_model_outputs`"						)
		Path(a_						).mkdir(exist_ok=a_						)
		model.save_pretrained(a_						)


if __name__ == "__main__":
					lowerCAmelCase									=   argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a model.pt on local filesystem."""
					)
					parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument(
					    """--hf_config""", default=None, type=str, help="""Which huggingface architecture to use: bart-large-xsum"""
					)
					lowerCAmelCase									=   parser.parse_args()
					convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)










'''simple docstring'''






lowerCAmelCase									=   [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]






def   __A    (				a_    :	int						):
		lowerCAmelCase  :     Optional[int]       						= 0
		while number:
				# Increased Speed Slightly by checking every 5 digits together.
				sum_of_digits_squared += DIGITS_SQUARED[number % 1_0_0_0_0_0]
				number //= 1_0_0_0_0_0

		return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
lowerCAmelCase									=   [None] * 10_00_00_00
lowerCAmelCase									=   True
lowerCAmelCase									=   False






def   __A    (				a_    :	int						):

		if CHAINS[number - 1] is not None:
				return CHAINS[number - 1]  # type: ignore

		lowerCAmelCase  :     Dict       						= chain(next_number(a_						)						)
		lowerCAmelCase  :     Union[str, Any]       						= number_chain

		while number < 1_0_0_0_0_0_0_0:
				lowerCAmelCase  :     Any       						= number_chain
				number *= 1_0

		return number_chain






def   __A    (				a_    :	int = 1_0_0_0_0_0_0_0						):
		for i in range(1   ,a_						):
				if CHAINS[i] is None:
						chain(i + 1						)

		return CHAINS[:number].count(a_						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(F'''{solution() = }''')



